{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_openjourney-bot","slug":"openjourney-bot","name":"Openjourney Bot","type":"webapp","url":"https://openjourneybot.com","page_url":"https://unfragile.ai/openjourney-bot","categories":["image-generation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_openjourney-bot__cap_0","uri":"capability://image.visual.text.to.4k.image.generation.with.diffusion.models","name":"text-to-4k-image-generation-with-diffusion-models","description":"Converts natural language text prompts into 4K resolution images (3840x2160 or equivalent) using latent diffusion model inference, likely leveraging fine-tuned Stable Diffusion or similar open-source architectures. The system tokenizes input prompts, encodes them through a CLIP-based text encoder, and iteratively denoises latent representations across multiple diffusion steps before upsampling to final 4K output. Architecture appears to batch-process requests through GPU-accelerated inference pipelines with built-in prompt optimization to handle complex, multi-concept descriptions.","intents":["Generate high-resolution marketing imagery from product descriptions without hiring photographers","Create concept art and visual mockups from written creative briefs","Produce consistent 4K backgrounds and assets for design projects at scale","Rapidly iterate on visual ideas by refining text prompts rather than manual editing"],"best_for":["Solo creators and small agencies needing fast asset generation","E-commerce businesses generating product photography alternatives","Content creators producing visual assets for social media and marketing"],"limitations":["Generation latency typically 30-120 seconds per image depending on queue and model load","4K output quality degrades with highly specific art direction or rare style combinations","No fine-tuning or custom model training available — limited to base model capabilities","Prompt engineering required for consistent results; vague descriptions produce unpredictable outputs"],"requires":["Active internet connection with stable bandwidth","Paid account with available credits/subscription balance","Modern browser supporting WebGL for preview rendering"],"input_types":["text (natural language prompts, 10-500 characters typical)","optional style/quality modifiers (aspect ratio, artistic style tags)"],"output_types":["PNG/JPEG image files at 4K resolution (3840x2160 or 4096x2160)","Metadata including generation parameters, seed, model version"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_1","uri":"capability://image.visual.in.platform.image.editing.and.inpainting","name":"in-platform-image-editing-and-inpainting","description":"Provides integrated image editing capabilities including selective region modification (inpainting), content-aware fill, and localized adjustments without requiring external software. The system likely uses masked diffusion inpainting where users define regions to modify, the model encodes the unmasked context, and iteratively refines only the masked area while preserving surrounding content. This approach maintains coherence with existing image elements and enables iterative refinement within a single interface.","intents":["Modify specific elements in generated images (e.g., change a person's clothing or background) without regenerating the entire image","Remove or replace unwanted objects while maintaining visual consistency","Extend or expand images beyond original boundaries using context-aware generation","Iteratively refine generated images without context-switching to Photoshop or external editors"],"best_for":["Designers and creators wanting rapid iteration without learning Photoshop","Teams needing quick asset modifications without specialized image editing skills","Hobbyists and small businesses optimizing for speed over pixel-perfect precision"],"limitations":["Inpainting quality degrades with large masked regions or complex object boundaries","No layer-based non-destructive editing — modifications are baked into output","Limited precision tools compared to traditional editors; brush selection and feathering may be basic","Inpainting can introduce artifacts or inconsistencies if masked region is too semantically distant from context"],"requires":["Generated or uploaded base image in PNG/JPEG format","Sufficient credits/subscription balance for inpainting inference","Browser with canvas/drawing API support for mask creation"],"input_types":["image (PNG/JPEG, any resolution up to 4K)","mask or selection (user-drawn or automatically generated)","text prompt describing desired modifications"],"output_types":["modified image (PNG/JPEG, same resolution as input)","edit history/version tracking (if supported)"],"categories":["image-visual","editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_2","uri":"capability://image.visual.image.enhancement.and.upscaling.pipeline","name":"image-enhancement-and-upscaling-pipeline","description":"Applies post-processing enhancement filters and optional upscaling to generated or user-provided images through a chained processing pipeline. The system likely uses super-resolution neural networks (e.g., Real-ESRGAN or similar) combined with color correction, sharpening, and artifact reduction algorithms. Enhancement can be applied automatically or selectively, with configurable intensity levels to balance detail preservation against over-processing artifacts.","intents":["Improve visual quality and clarity of generated images before export","Upscale lower-resolution source images to 4K without quality loss","Reduce compression artifacts and noise from generation or user uploads","Apply consistent enhancement presets across batches of images"],"best_for":["Creators needing final-output polish without external upscaling software","Batch processing workflows where consistent enhancement is required","Users working with lower-resolution source material needing quality improvement"],"limitations":["Upscaling introduces hallucinated details that may not match original intent","Enhancement presets are one-size-fits-all; limited per-image customization","Processing adds 10-30 seconds latency per image depending on enhancement intensity","Over-enhancement can introduce artifacts (halos, texture distortion) if settings are aggressive"],"requires":["Source image in PNG/JPEG format","Available credits for enhancement processing","Sufficient time budget for processing (typically 10-30 seconds per image)"],"input_types":["image (PNG/JPEG, any resolution)","enhancement preset selection (low/medium/high intensity or custom parameters)"],"output_types":["enhanced image (PNG/JPEG, same or higher resolution)","enhancement metadata (algorithm version, intensity applied)"],"categories":["image-visual","enhancement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_3","uri":"capability://text.generation.language.prompt.optimization.and.interpretation","name":"prompt-optimization-and-interpretation","description":"Analyzes user-provided text prompts and automatically optimizes them for improved generation quality through semantic understanding and prompt engineering heuristics. The system likely tokenizes input, identifies key concepts, detects style/quality modifiers, and reorders or augments prompts to align with model training patterns. This may include expanding vague descriptions, adding implicit quality tags, and reweighting concept importance to improve consistency and reduce ambiguity in model inference.","intents":["Improve generation quality without requiring users to learn prompt engineering techniques","Automatically detect and enhance style/quality intent from casual descriptions","Reduce failed or low-quality generations by optimizing prompts before inference","Enable non-technical users to achieve results comparable to experienced prompt engineers"],"best_for":["Beginner users unfamiliar with prompt engineering best practices","Teams wanting consistent quality without specialized AI expertise","Rapid prototyping workflows where prompt iteration overhead is undesirable"],"limitations":["Optimization heuristics may over-interpret user intent or add unintended concepts","Cannot recover from fundamentally ambiguous or contradictory prompts","Optimization may reduce user control over specific artistic direction","No transparency into optimization changes — users cannot see what was modified"],"requires":["Text prompt input (minimum 5-10 characters for meaningful optimization)","No additional configuration required"],"input_types":["text (natural language prompt, any length)"],"output_types":["optimized prompt (text, typically 20-30% longer than input)","generation parameters (aspect ratio, quality level, inferred style tags)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_4","uri":"capability://automation.workflow.batch.image.generation.with.credit.management","name":"batch-image-generation-with-credit-management","description":"Enables users to queue and process multiple image generation requests sequentially or in parallel, with integrated credit/subscription tracking and consumption accounting. The system likely maintains a job queue, distributes requests across available GPU resources, and tracks credit usage per generation (varying by resolution, model, and enhancement options). Users can monitor generation progress, cancel jobs, and view credit consumption in real-time through a dashboard interface.","intents":["Generate multiple variations or related images without manual re-submission","Process large asset libraries efficiently while monitoring cost","Create consistent image series with incremental prompt variations","Understand and control spending on image generation across team or project"],"best_for":["Content creators and agencies producing multiple assets per project","Teams needing cost visibility and budget control for AI generation","Workflows requiring consistent batches of related images"],"limitations":["Queue processing is sequential or limited-parallel; large batches may take hours","Credit pricing is opaque; unclear cost per image or how resolution/enhancement affects pricing","No batch discount or volume pricing transparency","Failed generations may consume credits without user recourse or automatic retry"],"requires":["Paid account with sufficient credit balance","Batch size typically limited to 10-100 images per submission (unknown exact limit)"],"input_types":["batch of text prompts (JSON, CSV, or UI form)","generation parameters (resolution, style, enhancement settings)"],"output_types":["generated images (PNG/JPEG files, organized by batch ID)","credit consumption report (total credits used, cost breakdown)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_5","uri":"capability://image.visual.style.and.aesthetic.preset.application","name":"style-and-aesthetic-preset-application","description":"Provides pre-configured style templates and aesthetic presets that users can apply to prompts to achieve consistent visual outcomes without manual style engineering. The system likely maintains a library of curated style descriptors (e.g., 'cinematic', 'oil painting', 'cyberpunk', 'photorealistic') that are automatically injected into prompts or used to condition model inference. Presets may include associated color palettes, composition guidelines, and quality modifiers that collectively shape the generation output.","intents":["Apply consistent visual style across multiple generated images without learning style terminology","Explore different artistic directions quickly by switching presets","Achieve specific aesthetic outcomes (e.g., photorealistic, illustration, 3D render) reliably","Reduce prompt engineering burden by using curated style templates"],"best_for":["Designers and creators wanting quick style exploration","Teams needing consistent branding or aesthetic across asset libraries","Non-technical users unfamiliar with art terminology or style descriptors"],"limitations":["Preset library is fixed; no custom style creation or fine-tuning","Presets may conflict with user intent or produce undesired combinations","Limited control over preset intensity or blending with other styles","Presets are generic and may not match niche or highly specific aesthetic requirements"],"requires":["Text prompt input","Selection of style preset from available library"],"input_types":["text prompt","style preset identifier (e.g., 'cinematic', 'oil-painting')"],"output_types":["generated image with applied style","style metadata (preset name, associated parameters)"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_6","uri":"capability://memory.knowledge.image.gallery.and.generation.history.management","name":"image-gallery-and-generation-history-management","description":"Maintains a persistent gallery of user-generated images with searchable metadata, generation parameters, and version history. The system likely stores images in cloud storage with indexed metadata (prompts, parameters, timestamps, enhancement settings), enabling users to browse, filter, and retrieve past generations. Users can view generation parameters, regenerate with modifications, or export images in multiple formats. History may include branching versions if users edited or re-generated from previous outputs.","intents":["Retrieve and reuse successful generations without re-prompting","Track generation parameters and prompts for reproducibility","Organize and categorize generated images for project management","Iterate on previous generations by modifying parameters and regenerating"],"best_for":["Creators managing large asset libraries across multiple projects","Teams needing centralized image storage and version tracking","Workflows requiring reproducibility and parameter documentation"],"limitations":["Gallery storage may be limited by subscription tier; unclear retention policies","Search and filtering capabilities likely basic (text search, date range, not semantic search)","No collaboration features for shared galleries or team access","Exporting large galleries may be slow or require batch export tools"],"requires":["Active account with sufficient storage quota","Browser with modern storage API support"],"input_types":["search query (text, date range, parameter filters)"],"output_types":["image list with metadata (prompts, parameters, timestamps)","individual images (PNG/JPEG, original resolution)","generation parameter export (JSON or CSV)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_7","uri":"capability://image.visual.aspect.ratio.and.composition.control","name":"aspect-ratio-and-composition-control","description":"Allows users to specify output image dimensions and aspect ratios (e.g., 16:9, 1:1, 9:16, custom) before generation, with the diffusion model conditioning on the target aspect ratio during inference. The system likely includes preset aspect ratios for common use cases (social media, print, cinema) and may provide composition guides or rule-of-thirds overlays to assist framing. The model adapts its generation strategy based on aspect ratio to optimize composition and content distribution.","intents":["Generate images in specific dimensions for targeted use cases (Instagram, YouTube, print)","Control composition and framing without post-crop distortion","Create consistent aspect ratios across image series","Optimize image generation for specific display contexts (mobile, desktop, billboard)"],"best_for":["Content creators producing images for specific platforms or media","Designers needing consistent dimensions across asset libraries","Marketing teams creating platform-specific imagery"],"limitations":["Aspect ratio conditioning may reduce quality or introduce composition artifacts for extreme ratios","Limited preset options; custom aspect ratios may not be supported","No advanced composition control (rule of thirds, golden ratio, focal point specification)","Aspect ratio changes may affect credit consumption (unclear pricing model)"],"requires":["Selection of aspect ratio from presets or custom input","No additional configuration required"],"input_types":["aspect ratio (preset or custom WxH dimensions)","text prompt"],"output_types":["image at specified aspect ratio (PNG/JPEG)","composition metadata (aspect ratio applied, dimensions)"],"categories":["image-visual","content-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_openjourney-bot__cap_8","uri":"capability://tool.use.integration.web.based.collaborative.workspace.interface","name":"web-based-collaborative-workspace-interface","description":"Provides a browser-based UI for image generation, editing, and management with real-time feedback and progress indication. The interface likely includes a prompt input area, generation parameters panel, live preview canvas, and gallery sidebar. The system uses WebSocket or polling for real-time status updates, allowing users to monitor generation progress and receive notifications when images are ready. The UI is optimized for both desktop and mobile browsers.","intents":["Generate and edit images without installing software or managing local files","Monitor generation progress and receive real-time status updates","Access image generation from any device with a web browser","Manage projects and assets through a unified web interface"],"best_for":["Users preferring cloud-based workflows without local installation","Teams needing browser-based access from multiple devices","Creators wanting quick iteration without software setup overhead"],"limitations":["Web interface performance depends on browser capabilities and network latency","No offline mode; requires persistent internet connection","File uploads and downloads may be slow for large batches","Browser compatibility issues may affect some features on older browsers"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Stable internet connection with sufficient bandwidth","JavaScript enabled"],"input_types":["text prompts (via text input)","image uploads (drag-and-drop or file picker)","parameter selections (dropdowns, sliders, presets)"],"output_types":["rendered UI with live preview","downloadable images (PNG/JPEG)","real-time progress notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active internet connection with stable bandwidth","Paid account with available credits/subscription balance","Modern browser supporting WebGL for preview rendering","Generated or uploaded base image in PNG/JPEG format","Sufficient credits/subscription balance for inpainting inference","Browser with canvas/drawing API support for mask creation","Source image in PNG/JPEG format","Available credits for enhancement processing","Sufficient time budget for processing (typically 10-30 seconds per image)","Text prompt input (minimum 5-10 characters for meaningful optimization)"],"failure_modes":["Generation latency typically 30-120 seconds per image depending on queue and model load","4K output quality degrades with highly specific art direction or rare style combinations","No fine-tuning or custom model training available — limited to base model capabilities","Prompt engineering required for consistent results; vague descriptions produce unpredictable outputs","Inpainting quality degrades with large masked regions or complex object boundaries","No layer-based non-destructive editing — modifications are baked into output","Limited precision tools compared to traditional editors; brush selection and feathering may be basic","Inpainting can introduce artifacts or inconsistencies if masked region is too semantically distant from context","Upscaling introduces hallucinated details that may not match original intent","Enhancement presets are one-size-fits-all; limited per-image customization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.859Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openjourney-bot","compare_url":"https://unfragile.ai/compare?artifact=openjourney-bot"}},"signature":"kByJ8gR5yyFhFhUtZJob3TbvR3p/9ATlDNVZEDS29bNX7OMhttBDbXBZrhBdtqS9rHj9V8fspQsgmwQ/zlpNCg==","signedAt":"2026-06-20T21:21:03.798Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openjourney-bot","artifact":"https://unfragile.ai/openjourney-bot","verify":"https://unfragile.ai/api/v1/verify?slug=openjourney-bot","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}